{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9afac148-f9ff-4d42-bec1-e2fec957a531",
   "metadata": {},
   "source": [
    "# 本节大纲\n",
    "1. 版本介绍, 10分钟\n",
    "2. 文档处理（DL & TS & OP）, 15分钟\n",
    "3. 语音模型（ASR & TTS）, 15分钟\n",
    "4. 绘图模型（Wanx & DALL·E)，15分钟\n",
    "5. 视频生成方案 (Runway & Self), 30分钟\n",
    "7. OpenAI 1106 发布总结，25分钟\n",
    "8. 答疑和总结，10分钟"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf945eb4-7f2d-48ef-8e22-785dcc3dfd58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d73b23e2-a436-4118-979c-73322b0f3fd1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54d7fedb-db63-4bc8-83a5-82c0f4285a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67d257a8-4f31-473f-aa9e-dca6ea532fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "!sudo apt install ffmpeg -y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8413d43c-7476-4e07-bc69-431a01ed9ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade langchain openai baidu-aip chardet pydub ffprobe ffmpeg pypdf dashscope pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b934c687-28bb-4983-9007-f822540b2388",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9cc78319-ea05-4c50-921f-98d8432bdedd",
   "metadata": {},
   "source": [
    "## LangChain App Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a89f11e-4811-48fd-a3df-70035605dad8",
   "metadata": {},
   "source": [
    "![Production-Oriented_LangChain](./resource/images/Production-Oriented_LangChain.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2769645e-4699-4ac5-b80b-4df9496a96dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f89d99f5-01f9-48e0-afbd-4d81ef1fd758",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80ac15c0-1f48-454a-a32a-41ba88864fb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3be27c1d-db75-4a2c-b933-6a512b66bb3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 修补下 1106 OpenAI 的发布对于 LangChain 的冲击\n",
    "\n",
    "!pip install openai==0.28.1 langchain==0.0.330"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035d8d16-14e5-4be8-8096-50800379a425",
   "metadata": {},
   "source": [
    "![Model_IO](./resource/images/Model_IO.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "458a7b04-45de-4b47-abfa-92654e10c984",
   "metadata": {},
   "source": [
    "### Document Loader "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2940cf24-880c-41c9-af69-a8ba6bec1219",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://python.langchain.com/docs/modules/data_connection/document_loaders/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9248151e-c207-4f21-93e6-199212243684",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import PyPDFLoader\n",
    "\n",
    "loader = PyPDFLoader(\"./resource/files/2210.03629.pdf\")\n",
    "pages = loader.load_and_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b96147d7-6a33-4b2a-8a9c-54f6fd3e4e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d361ccfa-dd28-40ff-bd4d-45ac57d6b9ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Published as a conference paper at ICLR 2023\\nREAC T: S YNERGIZING REASONING AND ACTING IN\\nLANGUAGE MODELS\\nShunyu Yao∗*,1, Jeffrey Zhao2, Dian Yu2, Nan Du2, Izhak Shafran2, Karthik Narasimhan1, Yuan Cao2\\n1Department of Computer Science, Princeton University\\n2Google Research, Brain team\\n1{shunyuy,karthikn}@princeton.edu\\n2{jeffreyzhao,dianyu,dunan,izhak,yuancao}@google.com\\nABSTRACT\\nWhile large language models (LLMs) have demonstrated impressive performance\\nacross tasks in language understanding and interactive decision making, their\\nabilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action\\nplan generation) have primarily been studied as separate topics. In this paper, we\\nexplore the use of LLMs to generate both reasoning traces and task-speciﬁc actions\\nin an interleaved manner, allowing for greater synergy between the two: reasoning\\ntraces help the model induce, track, and update action plans as well as handle\\nexceptions, while actions allow it to interface with and gather additional information\\nfrom external sources such as knowledge bases or environments. We apply our\\napproach, named ReAct , to a diverse set of language and decision making tasks\\nand demonstrate its effectiveness over state-of-the-art baselines in addition to\\nimproved human interpretability and trustworthiness. Concretely, on question\\nanswering (HotpotQA) and fact veriﬁcation (Fever), ReAct overcomes prevalent\\nissues of hallucination and error propagation in chain-of-thought reasoning by\\ninteracting with a simple Wikipedia API, and generating human-like task-solving\\ntrajectories that are more interpretable than baselines without reasoning traces.\\nFurthermore, on two interactive decision making benchmarks (ALFWorld and\\nWebShop), ReAct outperforms imitation and reinforcement learning methods by\\nan absolute success rate of 34% and 10% respectively, while being prompted with\\nonly one or two in-context examples.\\n1 I NTRODUCTION\\nA unique feature of human intelligence is the ability to seamlessly combine task-oriented actions with\\nverbal reasoning (or inner speech, Alderson-Day & Fernyhough, 2015), which has been theorized to\\nplay an important role in human cognition for enabling self-regulation or strategization (Vygotsky,\\n1987; Luria, 1965; Fernyhough, 2010) and maintaining a working memory (Baddeley, 1992). Con-\\nsider the example of cooking up a dish in the kitchen. Between any two speciﬁc actions, we may\\nreason in language in order to track progress (“now that everything is cut, I should heat up the pot of\\nwater”), to handle exceptions or adjust the plan according to the situation (“I don’t have salt, so let\\nme use soy sauce and pepper instead”), and to realize when external information is needed (“how do\\nI prepare dough? Let me search on the Internet”). We may also act (open a cookbook to read the\\nrecipe, open the fridge, check ingredients) to support the reasoning and to answer questions (“What\\ndish can I make right now?”). This tight synergy between “acting” and “reasoning” allows humans\\nto learn new tasks quickly and perform robust decision making or reasoning, even under previously\\nunseen circumstances or facing information uncertainties.\\nRecent results have hinted at the possibility of combining verbal reasoning with interactive decision\\nmaking in autonomous systems. On one hand, properly prompted large language models (LLMs)\\nhave demonstrated emergent capabilities to carry out several steps of reasoning traces to derive\\n∗Work during Google internship. Projet page with code: https://react-lm.github.io/ .\\n1arXiv:2210.03629v3  [cs.CL]  10 Mar 2023'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pages[0].page_content"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e23bda-6dae-4176-9ca3-c9088abdd003",
   "metadata": {},
   "source": [
    "### Text Splitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e3f081e-2fae-4a5e-91da-d0690f0f68a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://python.langchain.com/docs/modules/data_connection/document_transformers/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "964134aa-c796-4a23-9c3b-c2d8c43e2b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "\n",
    "text_splitter = CharacterTextSplitter(\n",
    "    separator = \"\\n\",\n",
    "    chunk_size = 1000,\n",
    "    chunk_overlap  = 200,\n",
    "    length_function = len\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c543b11a-9ceb-422e-a40d-8e39da920267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts = text_splitter.create_documents([pages[0].page_content])\n",
    "len(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52d2bf4f-fc2d-4306-9eff-d5ffe5ce1012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Published as a conference paper at ICLR 2023\\nREAC T: S YNERGIZING REASONING AND ACTING IN\\nLANGUAGE MODELS\\nShunyu Yao∗*,1, Jeffrey Zhao2, Dian Yu2, Nan Du2, Izhak Shafran2, Karthik Narasimhan1, Yuan Cao2\\n1Department of Computer Science, Princeton University\\n2Google Research, Brain team\\n1{shunyuy,karthikn}@princeton.edu\\n2{jeffreyzhao,dianyu,dunan,izhak,yuancao}@google.com\\nABSTRACT\\nWhile large language models (LLMs) have demonstrated impressive performance\\nacross tasks in language understanding and interactive decision making, their\\nabilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action\\nplan generation) have primarily been studied as separate topics. In this paper, we\\nexplore the use of LLMs to generate both reasoning traces and task-speciﬁc actions\\nin an interleaved manner, allowing for greater synergy between the two: reasoning\\ntraces help the model induce, track, and update action plans as well as handle'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts[0].page_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "593a59b5-9c52-4561-a1d5-eb9d0aba0eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'in an interleaved manner, allowing for greater synergy between the two: reasoning\\ntraces help the model induce, track, and update action plans as well as handle\\nexceptions, while actions allow it to interface with and gather additional information\\nfrom external sources such as knowledge bases or environments. We apply our\\napproach, named ReAct , to a diverse set of language and decision making tasks\\nand demonstrate its effectiveness over state-of-the-art baselines in addition to\\nimproved human interpretability and trustworthiness. Concretely, on question\\nanswering (HotpotQA) and fact veriﬁcation (Fever), ReAct overcomes prevalent\\nissues of hallucination and error propagation in chain-of-thought reasoning by\\ninteracting with a simple Wikipedia API, and generating human-like task-solving\\ntrajectories that are more interpretable than baselines without reasoning traces.\\nFurthermore, on two interactive decision making benchmarks (ALFWorld and'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts[1].page_content"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0a9bd63-6ed6-4fc6-9a2a-5bb98ba9a5e4",
   "metadata": {},
   "source": [
    "### Output Parsers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "50f179bd-6c84-44d7-8394-01a35e623338",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://python.langchain.com/docs/modules/model_io/output_parsers/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67286f42-9f43-47bb-a734-c2d41e4e3b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = PyPDFLoader(\"./resource/files/墨小问.pdf\")\n",
    "pages = loader.load_and_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c3cd37c1-772e-47a7-a8e4-bc4537f280c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "墨小问\n",
      "男|年龄：35岁|\n",
      "130-1111-6789|\n",
      "123987456@qq.com\n",
      "10年工作经验|求职意向：算法工程师|期望薪资：30-50K|期望城市：北京\n",
      "个人优势\n",
      "熟练Python语言，熟练使用Linux系统；\n",
      "熟练大语言模型的训练，复现ChatGPT以及大语言模型微调；\n",
      "熟练使用NumPy、Pandas、scikit-learn等工具进行数据分析和处理；\n",
      "掌握RNN，BERT，Transformer，GPT,HMM，CRF等深度学习常用模型的原理以及使用；\n",
      "熟悉线性回归、逻辑回归、SVM、聚类、降维、KNN等机器学习算法；\n",
      "了解自然语言处理(NLP)技术，包括知识图谱构建、智能机器人等。\n",
      "工作经历\n",
      "北京墨问医疗技术有限公司算法工程师2023.01-至今\n",
      "●负责收集数据并进行处理，包括数据清洗和数据的预处理，为模型训练做好准备\n",
      "●负责开发和实现自然语言处理NLP算法，以解决具体的业务问题\n",
      "●负责模型评估和指标的分析，进行模型的优化以提高模型的性能\n",
      "项目经历\n",
      "体检诊断结果生成算法工程师2023.05-至今\n",
      "项目介绍：\n",
      "随着人们生活水平的提高和健康意识的增强，越来越多的人开始关注自己的身体健康。定期进行全面\n",
      "的体检成为了预防和早期发现疾病的重要手段，每天体检的人都有很多，体检结束后医生要对所有的\n",
      "体检写一个诊断结果，为了缓解医生的压力，我们做了一个体检结果自动生成的工具。\n",
      "核心技术：ChatGLM3-6B、p-tuningv2微调、BLEU评估\n"
     ]
    }
   ],
   "source": [
    "print(pages[0].page_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb9dc84a-c457-4222-a3f9-5429033a68f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.chains import LLMChain \n",
    "from langchain.chat_models import ChatOpenAI\n",
    "\n",
    "llm = ChatOpenAI(\n",
    "    model_name='gpt-3.5-turbo-0613',\n",
    "    temperature=0, max_tokens=2048,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0db9dc2a-db83-4d15-975e-2eae5c15e4d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prompt Engineering\n",
    "template = \"\"\"\n",
    "请针对 >>> 和 <<< 中间的简历信息，抽取出模版中字段对应的值，如果不存在则输出 None，按照指定的模版格式输出JSON。\n",
    ">>> {context} <<<\n",
    "\n",
    "###\n",
    "  \"姓名\": \"\",\n",
    "  \"年龄\": \"\",\n",
    "  \"工作年限\": \"\",\n",
    "  \"手机号码\": \"\",\n",
    "  \"邮箱地址\": \"\",\n",
    "  \"求职意向\": [\"岗位\": \"\", \"地区\": \"\", \"薪酬\": \"\"], # 求职意向可以有多个\n",
    "###\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"context\"])\n",
    "\n",
    "chain = LLMChain(llm=llm, prompt=prompt, output_key=\"output\")\n",
    "\n",
    "result = chain.run(context=pages[0].page_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6084076c-c552-4b0d-8a59-09a29cf41b7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"姓名\": \"墨小问\",\n",
      "  \"年龄\": \"35岁\",\n",
      "  \"工作年限\": \"10年\",\n",
      "  \"手机号码\": \"130-1111-6789\",\n",
      "  \"邮箱地址\": \"123987456@qq.com\",\n",
      "  \"求职意向\": [\n",
      "    {\n",
      "      \"岗位\": \"算法工程师\",\n",
      "      \"地区\": \"北京\",\n",
      "      \"薪酬\": \"30-50K\"\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e06413f-7d0f-44fd-83e7-a83ab5a094d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92057871-dd3e-421f-a00f-eb9075815498",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "49e860b6-45bd-4dd7-9487-5f6cb3f8179f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LangChain\n",
    "\n",
    "from langchain.output_parsers import StructuredOutputParser, ResponseSchema\n",
    "\n",
    "response_schemas = [\n",
    "    ResponseSchema(name=\"姓名\", description=\"Name\"),\n",
    "    ResponseSchema(name=\"年龄\", description=\"Age\"),\n",
    "    ResponseSchema(name=\"工作年限\", description=\"Years of work\"),\n",
    "    ResponseSchema(name=\"手机号码\", description=\"Phone number\"),\n",
    "    ResponseSchema(name=\"邮箱地址\", description=\"Email address\"),\n",
    "]\n",
    "output_parser = StructuredOutputParser.from_response_schemas(response_schemas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "65e1627d-3773-40b9-b7a5-6d796b3a2237",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The output should be a markdown code snippet formatted in the following schema, including the leading and trailing \"```json\" and \"```\":\n",
      "\n",
      "```json\n",
      "{\n",
      "\t\"姓名\": string  // Name\n",
      "\t\"年龄\": string  // Age\n",
      "\t\"工作年限\": string  // Years of work\n",
      "\t\"手机号码\": string  // Phone number\n",
      "\t\"邮箱地址\": string  // Email address\n",
      "}\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "print(output_parser.get_format_instructions())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bebd04ce-88a5-43d7-a9a4-73ab55547dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "format_instructions = output_parser.get_format_instructions()\n",
    "\n",
    "prompt = PromptTemplate(\n",
    "    template=\"Get the person resume information from the body of the input text.\\n{format_instructions}\\n{resume}\",\n",
    "    input_variables=[\"resume\"],\n",
    "    partial_variables={\"format_instructions\": format_instructions}\n",
    ")\n",
    "\n",
    "chain = LLMChain(llm=llm, prompt=prompt, output_key=\"output\")\n",
    "\n",
    "result = chain.run(resume=pages[0].page_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "69b5268c-c132-423d-a31f-bd236e17e43a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "```json\n",
      "{\n",
      "\t\"姓名\": \"墨小问\",\n",
      "\t\"年龄\": \"35岁\",\n",
      "\t\"工作年限\": \"10年\",\n",
      "\t\"手机号码\": \"130-1111-6789\",\n",
      "\t\"邮箱地址\": \"123987456@qq.com\"\n",
      "}\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b11c5e3e-91fc-4caf-9aa0-ea991be3eb41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52a8f129-9d4f-47f6-a483-786106683750",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f88004ff-5d31-4b79-be5f-a950931f1633",
   "metadata": {},
   "outputs": [],
   "source": [
    "# OpenAI Function Call\n",
    "\n",
    "resumer_custom_functions = [\n",
    "    {\n",
    "        'name': 'extract_resume_info',\n",
    "        'description': 'Get the person resume information from the body of the input text',\n",
    "        'parameters': {\n",
    "            'type': 'object',\n",
    "            'properties': {\n",
    "                '姓名': {\n",
    "                    'type': 'string',\n",
    "                    'description': 'Name of the person'\n",
    "                },\n",
    "                '年龄': {\n",
    "                    'type': 'integer',\n",
    "                    'description': 'Age'\n",
    "                },\n",
    "                '工作年限': {\n",
    "                    'type': 'string',\n",
    "                    'description': 'Years of work'\n",
    "                },\n",
    "                '手机号码': {\n",
    "                    'type': 'string',\n",
    "                    'description': 'Phone Number'\n",
    "                },\n",
    "                '邮箱地址': {\n",
    "                    'type': 'string',\n",
    "                    'description': 'Email Address'\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "df40b589-ea57-45ae-930c-56d1268886f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "\n",
    "response = openai.ChatCompletion.create(\n",
    "    model = 'gpt-3.5-turbo',\n",
    "    messages = [{'role': 'user', 'content': pages[0].page_content}],\n",
    "    functions = resumer_custom_functions,\n",
    "    function_call = 'auto'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1e8470e5-55f9-4ec1-9f62-86539665eb46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "\"姓名\": \"墨小问\",\n",
      "\"年龄\": 35,\n",
      "\"手机号码\": \"130-1111-6789\",\n",
      "\"邮箱地址\": \"123987456@qq.com\",\n",
      "\"工作年限\": \"10年\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(response.choices[0].message.function_call.arguments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deeb9be4-6094-4871-aaee-29d25e237a31",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc21cbd0-c383-4d5c-8ff4-db16e2064db3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bb241ea5-04ea-4d19-8081-a6e94e63bdc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: openai in /opt/conda/lib/python3.11/site-packages (0.28.1)\n",
      "Collecting openai\n",
      "  Obtaining dependency information for openai from https://files.pythonhosted.org/packages/f7/86/ea99e3f966a639a3256e2f79d78cd63d45d5eb651a5457ccbdcfd9eed24a/openai-1.1.1-py3-none-any.whl.metadata\n",
      "  Using cached openai-1.1.1-py3-none-any.whl.metadata (16 kB)\n",
      "Requirement already satisfied: anyio<4,>=3.5.0 in /opt/conda/lib/python3.11/site-packages (from openai) (3.7.1)\n",
      "Requirement already satisfied: distro<2,>=1.7.0 in /opt/conda/lib/python3.11/site-packages (from openai) (1.8.0)\n",
      "Requirement already satisfied: httpx<1,>=0.23.0 in /opt/conda/lib/python3.11/site-packages (from openai) (0.25.1)\n",
      "Requirement already satisfied: pydantic<3,>=1.9.0 in /opt/conda/lib/python3.11/site-packages (from openai) (2.4.2)\n",
      "Requirement already satisfied: tqdm>4 in /opt/conda/lib/python3.11/site-packages (from openai) (4.66.1)\n",
      "Requirement already satisfied: typing-extensions<5,>=4.5 in /opt/conda/lib/python3.11/site-packages (from openai) (4.8.0)\n",
      "Requirement already satisfied: idna>=2.8 in /opt/conda/lib/python3.11/site-packages (from anyio<4,>=3.5.0->openai) (3.4)\n",
      "Requirement already satisfied: sniffio>=1.1 in /opt/conda/lib/python3.11/site-packages (from anyio<4,>=3.5.0->openai) (1.3.0)\n",
      "Requirement already satisfied: certifi in /opt/conda/lib/python3.11/site-packages (from httpx<1,>=0.23.0->openai) (2023.7.22)\n",
      "Requirement already satisfied: httpcore in /opt/conda/lib/python3.11/site-packages (from httpx<1,>=0.23.0->openai) (1.0.1)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in /opt/conda/lib/python3.11/site-packages (from pydantic<3,>=1.9.0->openai) (0.6.0)\n",
      "Requirement already satisfied: pydantic-core==2.10.1 in /opt/conda/lib/python3.11/site-packages (from pydantic<3,>=1.9.0->openai) (2.10.1)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /opt/conda/lib/python3.11/site-packages (from httpcore->httpx<1,>=0.23.0->openai) (0.14.0)\n",
      "Using cached openai-1.1.1-py3-none-any.whl (217 kB)\n",
      "Installing collected packages: openai\n",
      "  Attempting uninstall: openai\n",
      "    Found existing installation: openai 0.28.1\n",
      "    Uninstalling openai-0.28.1:\n",
      "      Successfully uninstalled openai-0.28.1\n",
      "Successfully installed openai-1.1.1\n"
     ]
    }
   ],
   "source": [
    "# 以上是需要使用到 1106 OpenAI 发布的新特性\n",
    "# 需要重启一下 Kernel\n",
    "\n",
    "!pip install --upgrade openai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0029bed-cbdb-44a4-b680-ed94c8376cd0",
   "metadata": {},
   "source": [
    "## Multimodal 多模态的世界"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e777cac9-ac0e-45ee-af0a-f85a7c404d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不仅文本类的语言模型\n",
    "# 还有语音、图片、视频...\n",
    "\n",
    "# 最简单的需求，需要让小墨可以听得懂语音，并且用语音进行回复用户问题"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6bbfa7-aade-4363-865c-a94ac7259134",
   "metadata": {},
   "source": [
    "### 语音"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "300f8f68-1edc-4abb-a866-c57b025f444c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Text to Speech 文字合成声音 \n",
    "# https://ai.baidu.com/ai-doc/SPEECH/plbxhh4be"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9758d816-2fd4-4f11-b8e1-b54b6e3102c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from aip import AipSpeech\n",
    "\n",
    "APP_ID = 'replace to your key'\n",
    "API_KEY = 'replace to your key'\n",
    "SECRET_KEY = 'replace to your key'\n",
    "\n",
    "client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "adbedcf5-416a-4513-852e-8bf71e81c57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "result  = client.synthesis('瑶瑶是墨问西东颜值最高的，遥遥领先', 'zh', 1, {\n",
    "    'vol': 5, 'per': 3\n",
    "})\n",
    "\n",
    "# 识别正确返回语音二进制 错误则返回dict 参照下面错误码\n",
    "if not isinstance(result, dict):\n",
    "    with open('audio.mp3', 'wb') as f:\n",
    "        f.write(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dc37dc02-7dbf-445d-bd45-966aa5c2fac3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12480/3320746076.py:2: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
      "  from IPython.core.display import display\n"
     ]
    },
    {
     "data": {
      "text/html": [
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\" type=\"audio/mpeg\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Audio\n",
    "from IPython.core.display import display\n",
    "\n",
    "display(Audio('./audio.mp3', autoplay=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8aaef9b4-370d-4f0b-9f4b-f5f9ada2b48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.BufferedRandom name='audio.wav'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pydub import AudioSegment\n",
    "\n",
    "# asr 输入要求是 pcm 或者是 wav 或者 amr\n",
    "AudioSegment.from_mp3(\"audio.mp3\").export(\"audio.wav\", format=\"wav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5f16b62f-0bb6-4e9b-a3b6-6ee76ad96120",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Automatic Speech Recognition 识别语音成文字\n",
    "#https://ai.baidu.com/ai-doc/SPEECH/plbxfq24s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6ecb99d3-452f-48ef-bb49-a0faaff34795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'corpus_no': '7298577741401248426',\n",
       " 'err_msg': 'success.',\n",
       " 'err_no': 0,\n",
       " 'result': ['瑶瑶是木问西东颜值最高的遥遥领先。'],\n",
       " 'sn': '874539326341699332552'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('./audio.wav', 'rb') as fp:\n",
    "    result = client.asr(fp.read(), 'wav', 16000, {\n",
    "        'dev_pid': 1537,\n",
    "    })\n",
    "result "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c67def-a934-467b-bb38-3ffa93980c8c",
   "metadata": {},
   "source": [
    "#### 也可以用开源的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2a3d281f-1e5e-4643-8677-1f703aa8c5ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TTS (Text to Speech)\n",
    "\n",
    "# https://huggingface.co/facebook/mms-tts\n",
    "# https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html\n",
    "# https://huggingface.co/spaces/mms-meta/MMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1e48eaf2-e939-41a9-b6dd-c8875503dc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ASR (Automatic Speech Recognition )\n",
    "\n",
    "# https://huggingface.co/suno/bark\n",
    "# https://huggingface.co/openai/whisper-large-v2\n",
    "# https://huggingface.co/spaces/hf-audio/open_asr_leaderboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2b58c38-a95f-4b2d-8f18-60210a2a50d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "85165e64-aa47-41aa-8c4b-ff06e968a5b1",
   "metadata": {},
   "source": [
    "### 图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "67c298ba-194a-4fcd-b9c0-3aa7fed00e0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 让小墨作画"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9b91f633-3274-45d9-bca0-1c9fe3f5dac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 阿里云 万相模型\n",
    "\n",
    "from http import HTTPStatus\n",
    "from urllib.parse import urlparse, unquote\n",
    "from pathlib import PurePosixPath\n",
    "import requests\n",
    "import dashscope\n",
    "\n",
    "def image_generate(prompt):\n",
    "    rsp = dashscope.ImageSynthesis.call(model=dashscope.ImageSynthesis.Models.wanx_v1,\n",
    "                              prompt=prompt,\n",
    "                              n=1,\n",
    "                              api_key='replace to your key',\n",
    "                              size='1024*1024')\n",
    "    if rsp.status_code == HTTPStatus.OK:\n",
    "        print(rsp.output)\n",
    "        print(rsp.usage)\n",
    "        for result in rsp.output.results:\n",
    "            file_name = PurePosixPath(unquote(urlparse(result.url).path)).parts[-1]\n",
    "            with open('./%s' % file_name, 'wb+') as f:\n",
    "                f.write(requests.get(result.url).content)\n",
    "    else:\n",
    "        print('Failed, status_code: %s, code: %s, message: %s' %\n",
    "              (rsp.status_code, rsp.code, rsp.message))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8c923b33-4186-4282-b974-03883488c6dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"task_id\": \"36ddbb7f-6d7c-41f9-9107-b80a02f730b8\", \"task_status\": \"SUCCEEDED\", \"results\": [{\"url\": \"https://dashscope-result-sh.oss-cn-shanghai.aliyuncs.com/1d/e9/20231107/723609ee/155da3ee-ccd9-4816-a04b-b0bb96601b8b-1.png?Expires=1699418984&OSSAccessKeyId=LTAI5tQZd8AEcZX6KZV4G8qL&Signature=zuhOC3%2FXC%2B%2BmR9SeDbcSXVADH7c%3D\"}, {\"url\": \"https://dashscope-result-bj.oss-cn-beijing.aliyuncs.com/1d/ec/20231107/8d820c8d/ec22a4c3-aa92-4bcd-a461-591338571576-1.png?Expires=1699418984&OSSAccessKeyId=LTAI5tQZd8AEcZX6KZV4G8qL&Signature=Seq6Ev%2FIAumX%2F9l3i%2B76dE4iMRY%3D\"}, {\"url\": \"https://dashscope-result-sh.oss-cn-shanghai.aliyuncs.com/1d/a2/20231107/723609ee/f8a7fd56-ba97-4379-8839-14062a345298-1.png?Expires=1699418984&OSSAccessKeyId=LTAI5tQZd8AEcZX6KZV4G8qL&Signature=aD4rmvMwbKp9FOTKNKihQwiu%2Fcs%3D\"}, {\"url\": \"https://dashscope-result-bj.oss-cn-beijing.aliyuncs.com/1d/6f/20231107/8d820c8d/e376bf65-3fa9-484d-a3aa-45941e1d7f49-1.png?Expires=1699418984&OSSAccessKeyId=LTAI5tQZd8AEcZX6KZV4G8qL&Signature=57kj9QhD4cmo5lH7WHxsSx01v3g%3D\"}], \"submit_time\": \"2023-11-07 12:49:22.959\", \"scheduled_time\": \"2023-11-07 12:49:22.986\", \"end_time\": \"2023-11-07 12:49:46.289\", \"task_metrics\": {\"TOTAL\": 4, \"SUCCEEDED\": 4, \"FAILED\": 0}}\n",
      "{\"image_count\": 4}\n"
     ]
    }
   ],
   "source": [
    "result = image_generate(\"一个科技感十足的机器人战士\")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fc543fd-d433-49d7-81eb-93ac54d00b7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81cbba9d-f612-44a3-bc41-dbf705d3012f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4240e344-a16c-45cf-8532-ef1997a3d009",
   "metadata": {},
   "outputs": [],
   "source": [
    "# OpenAI DALL·E 3\n",
    "# https://openai.com/dall-e-3\n",
    "from openai import OpenAI\n",
    "client = OpenAI()\n",
    "\n",
    "response = client.images.generate(\n",
    "  model=\"dall-e-3\",\n",
    "  prompt=\"一个科技感十足的机器人战士\",\n",
    "  n=1,\n",
    "  quality=\"standard\",\n",
    "  size=\"1024x1024\"  # 512x512 & 1024x1024\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08128805-252a-42f6-a06a-fee03bf5286a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://oaidalleapiprodscus.blob.core.windows.net/private/org-gC0E2TIUytmQoqdPh9UI1M6f/user-RD3trJdh6BRyp04tgKWg1Kti/img-Wy8sCHZiNpJb9kqbOFHTLEUe.png?st=2023-11-07T03%3A51%3A13Z&se=2023-11-07T05%3A51%3A13Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-11-06T18%3A17%3A48Z&ske=2023-11-07T18%3A17%3A48Z&sks=b&skv=2021-08-06&sig=LYYfzXIZjhHJ7%2BMt0CuPBAJVXZP1nnEJGqXvP70YBuU%3D'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.data[0].url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "439952f1-1a29-4e30-ae6a-b9531a9fbf50",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "153317db-1d70-4acd-a3be-112615bd8267",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97610f15-c318-45da-83a8-bbee064b8ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 但实际上，可以结合SD的私有化模型进行产出\n",
    "# Stable Diffusion\n",
    "# https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0\n",
    "\n",
    "# 例如一个手机壳设计公司\n",
    "# 可以通过对话的方式进行作业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8ae3de2-dfb6-4798-9925-2e6b4e3b7b07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "43a6a3e8-052f-44e7-900c-bf42e4714e61",
   "metadata": {},
   "source": [
    "### 视频"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a47685d-02a2-4548-a057-6a8445a4717f",
   "metadata": {},
   "source": [
    "#### Runway GEN 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb9a63f8-7903-4f1f-9688-c0a9d738917e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://research.runwayml.com/gen2\n",
    "# 实际上很难直接可以用于生产环境\n",
    "# 但是结合之前我们说学的，造一套更加普适通用的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c240c68e-dfd2-4923-9b1a-9c4a6f7b9619",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c702eb1-901b-4414-98e0-a67697c8af87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 视频生成流程架构"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ffdde8-4684-4711-8d8a-c307c9131655",
   "metadata": {},
   "source": [
    "![Video_Generation_Architecture](resource/images/Video_Generation_Architecture.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61394453-83d1-41e7-b98b-9708293de834",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Text Search Video Model\n",
    "# https://huggingface.co/Searchium-ai/clip4clip-webvid150k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4625579b-774e-4061-baf5-a4e2d917d9e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "737a8d0c-e33a-402f-9712-a3ea81ce21b6",
   "metadata": {},
   "source": [
    "#### 代码生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6a00bc7-ff7d-4ae6-b5e8-83a9dbc3362a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 开源\n",
    "# Salesforce - CodeT5\n",
    "# https://huggingface.co/Salesforce/codet5p-2b\n",
    "\n",
    "# 智谱 - CodeGeex\n",
    "# https://huggingface.co/THUDM/codegeex2-6b\n",
    "\n",
    "# Facebook - CodeLlama\n",
    "# https://huggingface.co/codellama/CodeLlama-34b-hf\n",
    "\n",
    "# 商用\n",
    "# Github Copilot\n",
    "# https://github.com/features/copilot\n",
    "\n",
    "# AWS CodeWhisperer\n",
    "# https://aws.amazon.com/cn/codewhisperer/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee240a97-acc5-4486-8050-8cec11460c30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d3f7550-3fb2-4fd2-b674-1135d45d5dcc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8d6dae9f-5d74-4516-aa74-412f21a0b9ab",
   "metadata": {},
   "source": [
    "#### 视觉对话"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71bf94ff-c40b-4a0c-ac73-894afaf8efb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 开源\n",
    "# THUDM - VisualGLM-6B\n",
    "# https://huggingface.co/THUDM/visualglm-6b\n",
    "\n",
    "# LLaVA\n",
    "# https://huggingface.co/liuhaotian/llava-v1.5-13b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4def34d7-d019-4db2-9511-558ddcda08f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11d20130-3a81-46a9-aac8-f59110c9b69d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5abd0fb7-a006-46d2-9c71-843eccae3d26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6e011b0b-e90f-468a-94a2-c119b51ca20b",
   "metadata": {},
   "source": [
    "### 来自 OpenAI DevDay (1106) 发布的一些新特性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a250fa0e-f09d-409e-85f3-3f3274081959",
   "metadata": {},
   "source": [
    "#### 新模型支持"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "51d48103-0620-40e1-9dda-7b31db80c734",
   "metadata": {},
   "outputs": [
    {
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       "      <td>(id, davinci-similarity)</td>\n",
       "      <td>(created, 1651172509)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
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       "      <td>(id, code-davinci-edit-001)</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(id, curie-similarity)</td>\n",
       "      <td>(created, 1651172510)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(id, babbage-search-document)</td>\n",
       "      <td>(created, 1651172510)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(id, curie-instruct-beta)</td>\n",
       "      <td>(created, 1649364042)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(id, text-search-ada-doc-001)</td>\n",
       "      <td>(created, 1651172507)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(id, davinci-instruct-beta)</td>\n",
       "      <td>(created, 1649364042)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(id, gpt-4)</td>\n",
       "      <td>(created, 1687882411)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(id, text-similarity-babbage-001)</td>\n",
       "      <td>(created, 1651172505)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(id, text-search-davinci-doc-001)</td>\n",
       "      <td>(created, 1651172505)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(id, babbage-similarity)</td>\n",
       "      <td>(created, 1651172505)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(id, text-embedding-ada-002)</td>\n",
       "      <td>(created, 1671217299)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-internal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(id, davinci-search-query)</td>\n",
       "      <td>(created, 1651172505)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(id, gpt-4-vision-preview)</td>\n",
       "      <td>(created, 1698894917)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(id, text-similarity-curie-001)</td>\n",
       "      <td>(created, 1651172507)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(id, text-davinci-001)</td>\n",
       "      <td>(created, 1649364042)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(id, text-search-davinci-query-001)</td>\n",
       "      <td>(created, 1651172505)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(id, ada-search-document)</td>\n",
       "      <td>(created, 1651172507)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(id, ada-code-search-code)</td>\n",
       "      <td>(created, 1651172505)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(id, babbage-002)</td>\n",
       "      <td>(created, 1692634615)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(id, davinci-002)</td>\n",
       "      <td>(created, 1692634301)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(id, tts-1)</td>\n",
       "      <td>(created, 1681940951)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-internal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(id, davinci-search-document)</td>\n",
       "      <td>(created, 1651172509)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(id, curie-search-document)</td>\n",
       "      <td>(created, 1651172508)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(id, babbage-code-search-code)</td>\n",
       "      <td>(created, 1651172509)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(id, gpt-3.5-turbo-1106)</td>\n",
       "      <td>(created, 1698959748)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(id, text-search-ada-query-001)</td>\n",
       "      <td>(created, 1651172505)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(id, code-search-ada-text-001)</td>\n",
       "      <td>(created, 1651172507)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(id, babbage-code-search-text)</td>\n",
       "      <td>(created, 1651172509)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(id, code-search-babbage-code-001)</td>\n",
       "      <td>(created, 1651172507)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(id, ada-search-query)</td>\n",
       "      <td>(created, 1651172505)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(id, ada-code-search-text)</td>\n",
       "      <td>(created, 1651172510)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>(id, tts-1-hd)</td>\n",
       "      <td>(created, 1699046015)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>(id, text-search-curie-query-001)</td>\n",
       "      <td>(created, 1651172509)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>(id, text-davinci-002)</td>\n",
       "      <td>(created, 1649880484)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>(id, tts-1-1106)</td>\n",
       "      <td>(created, 1699053241)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>(id, text-davinci-edit-001)</td>\n",
       "      <td>(created, 1649809179)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>(id, code-search-babbage-text-001)</td>\n",
       "      <td>(created, 1651172507)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>(id, tts-1-hd-1106)</td>\n",
       "      <td>(created, 1699053533)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>(id, gpt-3.5-turbo)</td>\n",
       "      <td>(created, 1677610602)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>(id, gpt-3.5-turbo-instruct-0914)</td>\n",
       "      <td>(created, 1694122472)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>(id, ada)</td>\n",
       "      <td>(created, 1649357491)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>(id, text-ada-001)</td>\n",
       "      <td>(created, 1649364042)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>(id, ada-similarity)</td>\n",
       "      <td>(created, 1651172507)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>(id, code-search-ada-code-001)</td>\n",
       "      <td>(created, 1651172507)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>(id, text-similarity-ada-001)</td>\n",
       "      <td>(created, 1651172505)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>(id, gpt-3.5-turbo-0301)</td>\n",
       "      <td>(created, 1677649963)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>(id, gpt-3.5-turbo-instruct)</td>\n",
       "      <td>(created, 1692901427)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>(id, text-search-curie-doc-001)</td>\n",
       "      <td>(created, 1651172509)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-dev)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>(id, text-davinci-003)</td>\n",
       "      <td>(created, 1669599635)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai-internal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>(id, gpt-4-0613)</td>\n",
       "      <td>(created, 1686588896)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>(id, text-curie-001)</td>\n",
       "      <td>(created, 1649364043)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>(id, curie)</td>\n",
       "      <td>(created, 1649359874)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>(id, gpt-4-0314)</td>\n",
       "      <td>(created, 1687882410)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>(id, davinci)</td>\n",
       "      <td>(created, 1649359874)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>(id, dall-e-2)</td>\n",
       "      <td>(created, 1698798177)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>(id, gpt-3.5-turbo-0613)</td>\n",
       "      <td>(created, 1686587434)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, openai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>(id, dall-e-3)</td>\n",
       "      <td>(created, 1698785189)</td>\n",
       "      <td>(object, model)</td>\n",
       "      <td>(owned_by, system)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      0                      1  \\\n",
       "0     (id, text-search-babbage-doc-001)  (created, 1651172509)   \n",
       "1          (id, gpt-3.5-turbo-16k-0613)  (created, 1685474247)   \n",
       "2              (id, curie-search-query)  (created, 1651172509)   \n",
       "3               (id, gpt-3.5-turbo-16k)  (created, 1683758102)   \n",
       "4   (id, text-search-babbage-query-001)  (created, 1651172509)   \n",
       "5                         (id, babbage)  (created, 1649358449)   \n",
       "6            (id, babbage-search-query)  (created, 1651172509)   \n",
       "7                (id, text-babbage-001)  (created, 1649364043)   \n",
       "8                       (id, whisper-1)  (created, 1677532384)   \n",
       "9     (id, text-similarity-davinci-001)  (created, 1651172505)   \n",
       "10             (id, gpt-4-1106-preview)  (created, 1698957206)   \n",
       "11             (id, davinci-similarity)  (created, 1651172509)   \n",
       "12          (id, code-davinci-edit-001)  (created, 1649880484)   \n",
       "13               (id, curie-similarity)  (created, 1651172510)   \n",
       "14        (id, babbage-search-document)  (created, 1651172510)   \n",
       "15            (id, curie-instruct-beta)  (created, 1649364042)   \n",
       "16        (id, text-search-ada-doc-001)  (created, 1651172507)   \n",
       "17          (id, davinci-instruct-beta)  (created, 1649364042)   \n",
       "18                          (id, gpt-4)  (created, 1687882411)   \n",
       "19    (id, text-similarity-babbage-001)  (created, 1651172505)   \n",
       "20    (id, text-search-davinci-doc-001)  (created, 1651172505)   \n",
       "21             (id, babbage-similarity)  (created, 1651172505)   \n",
       "22         (id, text-embedding-ada-002)  (created, 1671217299)   \n",
       "23           (id, davinci-search-query)  (created, 1651172505)   \n",
       "24           (id, gpt-4-vision-preview)  (created, 1698894917)   \n",
       "25      (id, text-similarity-curie-001)  (created, 1651172507)   \n",
       "26               (id, text-davinci-001)  (created, 1649364042)   \n",
       "27  (id, text-search-davinci-query-001)  (created, 1651172505)   \n",
       "28            (id, ada-search-document)  (created, 1651172507)   \n",
       "29           (id, ada-code-search-code)  (created, 1651172505)   \n",
       "30                    (id, babbage-002)  (created, 1692634615)   \n",
       "31                    (id, davinci-002)  (created, 1692634301)   \n",
       "32                          (id, tts-1)  (created, 1681940951)   \n",
       "33        (id, davinci-search-document)  (created, 1651172509)   \n",
       "34          (id, curie-search-document)  (created, 1651172508)   \n",
       "35       (id, babbage-code-search-code)  (created, 1651172509)   \n",
       "36             (id, gpt-3.5-turbo-1106)  (created, 1698959748)   \n",
       "37      (id, text-search-ada-query-001)  (created, 1651172505)   \n",
       "38       (id, code-search-ada-text-001)  (created, 1651172507)   \n",
       "39       (id, babbage-code-search-text)  (created, 1651172509)   \n",
       "40   (id, code-search-babbage-code-001)  (created, 1651172507)   \n",
       "41               (id, ada-search-query)  (created, 1651172505)   \n",
       "42           (id, ada-code-search-text)  (created, 1651172510)   \n",
       "43                       (id, tts-1-hd)  (created, 1699046015)   \n",
       "44    (id, text-search-curie-query-001)  (created, 1651172509)   \n",
       "45               (id, text-davinci-002)  (created, 1649880484)   \n",
       "46                     (id, tts-1-1106)  (created, 1699053241)   \n",
       "47          (id, text-davinci-edit-001)  (created, 1649809179)   \n",
       "48   (id, code-search-babbage-text-001)  (created, 1651172507)   \n",
       "49                  (id, tts-1-hd-1106)  (created, 1699053533)   \n",
       "50                  (id, gpt-3.5-turbo)  (created, 1677610602)   \n",
       "51    (id, gpt-3.5-turbo-instruct-0914)  (created, 1694122472)   \n",
       "52                            (id, ada)  (created, 1649357491)   \n",
       "53                   (id, text-ada-001)  (created, 1649364042)   \n",
       "54                 (id, ada-similarity)  (created, 1651172507)   \n",
       "55       (id, code-search-ada-code-001)  (created, 1651172507)   \n",
       "56        (id, text-similarity-ada-001)  (created, 1651172505)   \n",
       "57             (id, gpt-3.5-turbo-0301)  (created, 1677649963)   \n",
       "58         (id, gpt-3.5-turbo-instruct)  (created, 1692901427)   \n",
       "59      (id, text-search-curie-doc-001)  (created, 1651172509)   \n",
       "60               (id, text-davinci-003)  (created, 1669599635)   \n",
       "61                     (id, gpt-4-0613)  (created, 1686588896)   \n",
       "62                 (id, text-curie-001)  (created, 1649364043)   \n",
       "63                          (id, curie)  (created, 1649359874)   \n",
       "64                     (id, gpt-4-0314)  (created, 1687882410)   \n",
       "65                        (id, davinci)  (created, 1649359874)   \n",
       "66                       (id, dall-e-2)  (created, 1698798177)   \n",
       "67             (id, gpt-3.5-turbo-0613)  (created, 1686587434)   \n",
       "68                       (id, dall-e-3)  (created, 1698785189)   \n",
       "\n",
       "                  2                            3  \n",
       "0   (object, model)       (owned_by, openai-dev)  \n",
       "1   (object, model)           (owned_by, openai)  \n",
       "2   (object, model)       (owned_by, openai-dev)  \n",
       "3   (object, model)  (owned_by, openai-internal)  \n",
       "4   (object, model)       (owned_by, openai-dev)  \n",
       "5   (object, model)           (owned_by, openai)  \n",
       "6   (object, model)       (owned_by, openai-dev)  \n",
       "7   (object, model)           (owned_by, openai)  \n",
       "8   (object, model)  (owned_by, openai-internal)  \n",
       "9   (object, model)       (owned_by, openai-dev)  \n",
       "10  (object, model)           (owned_by, system)  \n",
       "11  (object, model)       (owned_by, openai-dev)  \n",
       "12  (object, model)           (owned_by, openai)  \n",
       "13  (object, model)       (owned_by, openai-dev)  \n",
       "14  (object, model)       (owned_by, openai-dev)  \n",
       "15  (object, model)           (owned_by, openai)  \n",
       "16  (object, model)       (owned_by, openai-dev)  \n",
       "17  (object, model)           (owned_by, openai)  \n",
       "18  (object, model)           (owned_by, openai)  \n",
       "19  (object, model)       (owned_by, openai-dev)  \n",
       "20  (object, model)       (owned_by, openai-dev)  \n",
       "21  (object, model)       (owned_by, openai-dev)  \n",
       "22  (object, model)  (owned_by, openai-internal)  \n",
       "23  (object, model)       (owned_by, openai-dev)  \n",
       "24  (object, model)           (owned_by, system)  \n",
       "25  (object, model)       (owned_by, openai-dev)  \n",
       "26  (object, model)           (owned_by, openai)  \n",
       "27  (object, model)       (owned_by, openai-dev)  \n",
       "28  (object, model)       (owned_by, openai-dev)  \n",
       "29  (object, model)       (owned_by, openai-dev)  \n",
       "30  (object, model)           (owned_by, system)  \n",
       "31  (object, model)           (owned_by, system)  \n",
       "32  (object, model)  (owned_by, openai-internal)  \n",
       "33  (object, model)       (owned_by, openai-dev)  \n",
       "34  (object, model)       (owned_by, openai-dev)  \n",
       "35  (object, model)       (owned_by, openai-dev)  \n",
       "36  (object, model)           (owned_by, system)  \n",
       "37  (object, model)       (owned_by, openai-dev)  \n",
       "38  (object, model)       (owned_by, openai-dev)  \n",
       "39  (object, model)       (owned_by, openai-dev)  \n",
       "40  (object, model)       (owned_by, openai-dev)  \n",
       "41  (object, model)       (owned_by, openai-dev)  \n",
       "42  (object, model)       (owned_by, openai-dev)  \n",
       "43  (object, model)           (owned_by, system)  \n",
       "44  (object, model)       (owned_by, openai-dev)  \n",
       "45  (object, model)           (owned_by, openai)  \n",
       "46  (object, model)           (owned_by, system)  \n",
       "47  (object, model)           (owned_by, openai)  \n",
       "48  (object, model)       (owned_by, openai-dev)  \n",
       "49  (object, model)           (owned_by, system)  \n",
       "50  (object, model)           (owned_by, openai)  \n",
       "51  (object, model)           (owned_by, system)  \n",
       "52  (object, model)           (owned_by, openai)  \n",
       "53  (object, model)           (owned_by, openai)  \n",
       "54  (object, model)       (owned_by, openai-dev)  \n",
       "55  (object, model)       (owned_by, openai-dev)  \n",
       "56  (object, model)       (owned_by, openai-dev)  \n",
       "57  (object, model)           (owned_by, openai)  \n",
       "58  (object, model)           (owned_by, system)  \n",
       "59  (object, model)       (owned_by, openai-dev)  \n",
       "60  (object, model)  (owned_by, openai-internal)  \n",
       "61  (object, model)           (owned_by, openai)  \n",
       "62  (object, model)           (owned_by, openai)  \n",
       "63  (object, model)           (owned_by, openai)  \n",
       "64  (object, model)           (owned_by, openai)  \n",
       "65  (object, model)           (owned_by, openai)  \n",
       "66  (object, model)           (owned_by, system)  \n",
       "67  (object, model)           (owned_by, openai)  \n",
       "68  (object, model)           (owned_by, system)  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from openai import OpenAI\n",
    "\n",
    "client = OpenAI()\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.DataFrame(client.models.list().data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d1f8ca-5e7b-4e04-a16b-4708f3b19223",
   "metadata": {},
   "source": [
    "#### TTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19223de7-9205-4d0b-8f46-bca8f3e98505",
   "metadata": {},
   "outputs": [],
   "source": [
    "# voice: alloy, echo, fable, onyx, nova, and shimmer\n",
    "# response_format: mp3, opus, aac and flac\n",
    "# speed: 0.25 to 4.0\n",
    "\n",
    "from openai import OpenAI\n",
    "client = OpenAI()\n",
    "\n",
    "response = client.audio.speech.create(\n",
    "  model=\"tts-1-hd-1106\",\n",
    "  voice=\"nova\",\n",
    "  input=\"墨问西东公司是一家伟大的火箭发射服务提供商，我们致力给人类移民火星带来新突破\"\n",
    ")\n",
    "\n",
    "response.stream_to_file('./speech.mp3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e894f3fb-903a-498b-b546-d2142e727045",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12575/680462211.py:2: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
      "  from IPython.core.display import display\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source 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\" type=\"audio/mpeg\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Audio\n",
    "from IPython.core.display import display\n",
    "\n",
    "display(Audio('./speech.mp3', autoplay=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60fafdf5-cfec-4a18-9753-3d7d109d9632",
   "metadata": {},
   "source": [
    "#### ASR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "deff4dc7-d1d6-4138-baae-2537ddb8f561",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "client = OpenAI()\n",
    "\n",
    "audio_file= open('./speech.mp3', \"rb\")\n",
    "transcript = client.audio.transcriptions.create(\n",
    "  model=\"whisper-1\", \n",
    "  file=audio_file,\n",
    "  response_format=\"srt\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00b4536d-ed55-44e5-acaa-b330a04bbf00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1\\n00:00:00,000 --> 00:00:04,400\\n莫問西東公司是一家偉大的火箭發射服務提供商\\n\\n2\\n00:00:04,400 --> 00:00:08,200\\n我們致力給人類移民火星帶來新突破\\n\\n\\n'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9944aa52-952c-4c93-aa7d-46c7c106914d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "56dc497c-bfcd-4e6f-9dd4-5cf42d240f34",
   "metadata": {},
   "source": [
    "#### GPT-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "527f83cf-ec90-42e2-af76-3807fca4bbc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "client = OpenAI()\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"gpt-4-1106-preview\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"你是谁，你的最新训练数据是什么时候的\"},\n",
    "    ],\n",
    "    temperature=0.1, \n",
    "    max_tokens=1024\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e6f9d43e-cd8d-4ffd-9448-d74fab8e896e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'我是一个人工智能助手，由 OpenAI 开发。我的最新训练数据的截止日期是 2021 年，但我无法提供具体的最后更新日期。我的知识库不包括在此之后发生的事件或信息更新。如果你有任何问题，我会尽力根据我所受的训练来回答。'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "28c2fc4c-82e2-423f-ab4c-071509bfc549",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model=\"gpt-4-1106-preview\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"哪一个球队获得了2023年亚洲杯女足冠军\"},\n",
    "    ],\n",
    "    temperature=0.1, \n",
    "    max_tokens=1024\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "65035f6c-bc9b-4589-9f12-819271382379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'截至我所知的信息截止日期（2023年），2022年亚洲杯女足的冠军是中国女足，他们在决赛中击败了韩国女足。但是，关于2023年亚洲杯女足的冠军信息，由于比赛尚未举行或者我的知识库尚未更新，我无法提供确切的答案。通常情况下，亚洲杯女足是每四年举办一次，所以下一届应该是在2026年。如果您是指2023年的其他女足比赛或者最新的信息，请提供更具体的问题或者查找最新的体育新闻更新。'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a23ddf58-3672-4f28-8200-3684b5bda2b3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "35182ff9-74e9-4640-9377-2aa76d62eb45",
   "metadata": {},
   "source": [
    "#### JSON Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f27199e1-da8e-461e-b29b-95ee4712d5b0",
   "metadata": {},
   "outputs": [
    {
     "ename": "BadRequestError",
     "evalue": "Error code: 400 - {'error': {'message': \"'messages' must contain the word 'json' in some form, to use 'response_format' of type 'json_object'.\", 'type': 'invalid_request_error', 'param': 'messages', 'code': None}}",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mBadRequestError\u001B[0m                           Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[21], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mchat\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompletions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m      2\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mgpt-4-1106-preview\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m      3\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmessages\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\n\u001B[1;32m      4\u001B[0m \u001B[43m        \u001B[49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mrole\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43muser\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcontent\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m哪一个球队获得了2022年亚洲杯女足冠军\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m      5\u001B[0m \u001B[43m    \u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m      6\u001B[0m \u001B[43m    \u001B[49m\u001B[43mtemperature\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0.1\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\n\u001B[1;32m      7\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmax_tokens\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1024\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m      8\u001B[0m \u001B[43m    \u001B[49m\u001B[43mresponse_format\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mtype\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mjson_object\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m      9\u001B[0m \u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m/opt/conda/lib/python3.11/site-packages/openai/_utils/_utils.py:299\u001B[0m, in \u001B[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m    297\u001B[0m             msg \u001B[38;5;241m=\u001B[39m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMissing required argument: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mquote(missing[\u001B[38;5;241m0\u001B[39m])\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    298\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(msg)\n\u001B[0;32m--> 299\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m/opt/conda/lib/python3.11/site-packages/openai/resources/chat/completions.py:556\u001B[0m, in \u001B[0;36mCompletions.create\u001B[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, max_tokens, n, presence_penalty, response_format, seed, stop, stream, temperature, tool_choice, tools, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001B[0m\n\u001B[1;32m    512\u001B[0m \u001B[38;5;129m@required_args\u001B[39m([\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmessages\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel\u001B[39m\u001B[38;5;124m\"\u001B[39m], [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmessages\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstream\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n\u001B[1;32m    513\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcreate\u001B[39m(\n\u001B[1;32m    514\u001B[0m     \u001B[38;5;28mself\u001B[39m,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    554\u001B[0m     timeout: \u001B[38;5;28mfloat\u001B[39m \u001B[38;5;241m|\u001B[39m httpx\u001B[38;5;241m.\u001B[39mTimeout \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m|\u001B[39m NotGiven \u001B[38;5;241m=\u001B[39m NOT_GIVEN,\n\u001B[1;32m    555\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m ChatCompletion \u001B[38;5;241m|\u001B[39m Stream[ChatCompletionChunk]:\n\u001B[0;32m--> 556\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_post\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    557\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m/chat/completions\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m    558\u001B[0m \u001B[43m        \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmaybe_transform\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    559\u001B[0m \u001B[43m            \u001B[49m\u001B[43m{\u001B[49m\n\u001B[1;32m    560\u001B[0m \u001B[43m                \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmessages\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mmessages\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    561\u001B[0m \u001B[43m                \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmodel\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    562\u001B[0m \u001B[43m                \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mfrequency_penalty\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mfrequency_penalty\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    563\u001B[0m \u001B[43m                \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mfunction_call\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mfunction_call\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    564\u001B[0m \u001B[43m                \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mfunctions\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mfunctions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    565\u001B[0m \u001B[43m                \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mlogit_bias\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m 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\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mresponse_format\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mresponse_format\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    570\u001B[0m \u001B[43m                \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mseed\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mseed\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    571\u001B[0m \u001B[43m                \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mstop\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    572\u001B[0m \u001B[43m                \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mstream\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m 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\u001B[49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmake_request_options\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    582\u001B[0m \u001B[43m            \u001B[49m\u001B[43mextra_headers\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mextra_headers\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mextra_query\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mextra_query\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mextra_body\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mextra_body\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\n\u001B[1;32m    583\u001B[0m \u001B[43m        \u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    584\u001B[0m \u001B[43m        \u001B[49m\u001B[43mcast_to\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mChatCompletion\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    585\u001B[0m \u001B[43m        \u001B[49m\u001B[43mstream\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m    586\u001B[0m \u001B[43m        \u001B[49m\u001B[43mstream_cls\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mStream\u001B[49m\u001B[43m[\u001B[49m\u001B[43mChatCompletionChunk\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    587\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m/opt/conda/lib/python3.11/site-packages/openai/_base_client.py:1055\u001B[0m, in \u001B[0;36mSyncAPIClient.post\u001B[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001B[0m\n\u001B[1;32m   1041\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpost\u001B[39m(\n\u001B[1;32m   1042\u001B[0m     \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m   1043\u001B[0m     path: \u001B[38;5;28mstr\u001B[39m,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m   1050\u001B[0m     stream_cls: \u001B[38;5;28mtype\u001B[39m[_StreamT] \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m   1051\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m ResponseT \u001B[38;5;241m|\u001B[39m _StreamT:\n\u001B[1;32m   1052\u001B[0m     opts \u001B[38;5;241m=\u001B[39m FinalRequestOptions\u001B[38;5;241m.\u001B[39mconstruct(\n\u001B[1;32m   1053\u001B[0m         method\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpost\u001B[39m\u001B[38;5;124m\"\u001B[39m, url\u001B[38;5;241m=\u001B[39mpath, json_data\u001B[38;5;241m=\u001B[39mbody, files\u001B[38;5;241m=\u001B[39mto_httpx_files(files), \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39moptions\n\u001B[1;32m   1054\u001B[0m     )\n\u001B[0;32m-> 1055\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m cast(ResponseT, \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcast_to\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mopts\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstream\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstream_cls\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream_cls\u001B[49m\u001B[43m)\u001B[49m)\n",
      "File \u001B[0;32m/opt/conda/lib/python3.11/site-packages/openai/_base_client.py:834\u001B[0m, in \u001B[0;36mSyncAPIClient.request\u001B[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001B[0m\n\u001B[1;32m    825\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mrequest\u001B[39m(\n\u001B[1;32m    826\u001B[0m     \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m    827\u001B[0m     cast_to: Type[ResponseT],\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    832\u001B[0m     stream_cls: \u001B[38;5;28mtype\u001B[39m[_StreamT] \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m    833\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m ResponseT \u001B[38;5;241m|\u001B[39m _StreamT:\n\u001B[0;32m--> 834\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_request\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    835\u001B[0m \u001B[43m        \u001B[49m\u001B[43mcast_to\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcast_to\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    836\u001B[0m \u001B[43m        \u001B[49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    837\u001B[0m \u001B[43m        \u001B[49m\u001B[43mstream\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    838\u001B[0m \u001B[43m        \u001B[49m\u001B[43mstream_cls\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream_cls\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    839\u001B[0m \u001B[43m        \u001B[49m\u001B[43mremaining_retries\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mremaining_retries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    840\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m/opt/conda/lib/python3.11/site-packages/openai/_base_client.py:877\u001B[0m, in \u001B[0;36mSyncAPIClient._request\u001B[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001B[0m\n\u001B[1;32m    874\u001B[0m     \u001B[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001B[39;00m\n\u001B[1;32m    875\u001B[0m     \u001B[38;5;66;03m# to completion before attempting to access the response text.\u001B[39;00m\n\u001B[1;32m    876\u001B[0m     err\u001B[38;5;241m.\u001B[39mresponse\u001B[38;5;241m.\u001B[39mread()\n\u001B[0;32m--> 877\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_make_status_error_from_response(err\u001B[38;5;241m.\u001B[39mresponse) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m    878\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m httpx\u001B[38;5;241m.\u001B[39mTimeoutException \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m    879\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m retries \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m:\n",
      "\u001B[0;31mBadRequestError\u001B[0m: Error code: 400 - {'error': {'message': \"'messages' must contain the word 'json' in some form, to use 'response_format' of type 'json_object'.\", 'type': 'invalid_request_error', 'param': 'messages', 'code': None}}"
     ]
    }
   ],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model=\"gpt-4-1106-preview\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"哪一个球队获得了2022年亚洲杯女足冠军\"},\n",
    "    ],\n",
    "    temperature=0.1, \n",
    "    max_tokens=1024,\n",
    "    response_format={\"type\": \"json_object\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07f0900c-1078-45e0-a7cf-ce78bf68923f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 报错了，因为还是需要在 Prompt 加入 JSON 字眼的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1c555709-85dc-4f85-baaf-5166a23b692a",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model=\"gpt-4-1106-preview\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"哪一个球队获得了2022年亚洲杯女足冠军, 使用 JSON 格式输出\"},\n",
    "    ],\n",
    "    temperature=0.1, \n",
    "    max_tokens=1024,\n",
    "    response_format={\"type\": \"json_object\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dc87f6f4-66ba-4021-a75b-f5f541e1a932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\\n  \"tournament\": \"2022年亚洲杯女足\",\\n  \"champion\": \"中国女足\",\\n  \"country\": \"中国\"\\n}'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26332d34-176c-4865-83a0-f82a9f79b587",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d9c49e3b-2e2d-40e3-a97d-55e6791adad8",
   "metadata": {},
   "source": [
    "#### GPT-4 Vision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "26d0d803-5eaa-44a9-a37d-88759400a541",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_url = 'https://pics4.baidu.com/feed/d000baa1cd11728b6facd8fe932d21c7c1fd2c47.jpeg?token=5f5be7f05852ab940b42b8682674ce89'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b6f8892d-8698-4dd5-b6fb-bbe4135fade2",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model=\"gpt-4-vision-preview\",\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\"type\": \"text\", \"text\": \"这个图片的猪肉有问题吗？可以给人吃吗\"},\n",
    "                {\n",
    "                    \"type\": \"image_url\",\n",
    "                    \"image_url\": image_url,\n",
    "                },\n",
    "            ],\n",
    "        }\n",
    "    ],\n",
    "    max_tokens=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "54b986d3-c5fd-4697-b539-3d4ff283c5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'这张图片上的猪肉看起来表面有一些发霉的迹象，特别是有些部分覆盖着白色或绿色的霉斑。食物发霉通常意味着它可能已经被细菌或其他微生物污染了，这可能使其不适合食用。消费发霉的食品可能导致食物中毒或其他健康问题。\\n\\n在食用任何看起来或闻起来可能不新鲜或变质的食物之前，最好的做法是严格遵循食品安全标准，并咨询专业人士。如果有任何怀疑，出于安全考虑，不应该食用这些猪肉。'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cf5a19b0-12f2-4dbc-927f-5f07376be6f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_url = 'https://p3.toutiaoimg.com/large/pgc-image/8af5e39169e6473a84630d0bacf896e5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f9fae823-a9e3-4021-af9e-6b7f9eec0e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model=\"gpt-4-vision-preview\",\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\"type\": \"text\", \"text\": \"这个图片的猪肉有问题吗？可以给人吃吗\"},\n",
    "                {\n",
    "                    \"type\": \"image_url\",\n",
    "                    \"image_url\": image_url,\n",
    "                },\n",
    "            ],\n",
    "        }\n",
    "    ],\n",
    "    max_tokens=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "49f55e58-f58b-4b66-9b83-c45b32a7a263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'对不起，我无法对食物的安全性进行实际的评估，因为我不是食品安全专家，也不能亲自检验或嗅闻食物。但是，这张图片上的猪肉在视觉上看起来是新鲜的。猪肉颜色均匀，红润而不是灰暗，脂肪部分白净，这些都通常是新鲜猪肉的特征。不过，最终判断食物是否适宜食用，应基于多种因素，包括但不限于产品的储存条件、保质期以及食物是否经过适当的处理。如果你对猪肉的安全性有疑问，建议咨询专业人士或依照食品安全准则进行检查。'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3498c8f-cb72-41eb-8c52-b76c543c1f83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "56698d5b-b704-4040-a74f-f7947b66a740",
   "metadata": {},
   "source": [
    "#### GPT-3.5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c7b74399-7bbf-4c87-a00d-bce789fb09e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model=\"gpt-3.5-turbo-1106\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"哪一个球队获得了2022年足球的世界杯冠军\"},\n",
    "    ],\n",
    "    temperature=0.1, \n",
    "    max_tokens=1024\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b3d6a1e6-7c41-4ef4-a500-707862e47322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'很抱歉，我无法回答关于2022年足球世界杯的问题，因为我无法预测未来事件。'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d879926a-f860-4724-8a21-edf97439da4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b5d894d3-00d2-4e3c-8afd-6c9b875c84ae",
   "metadata": {},
   "source": [
    "#### Assistants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "57eca78a-d745-439f-b525-2dda0e199801",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12575/2277211884.py:1: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.4/migration/\n",
      "  client.beta.assistants.list().dict()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'data': [{'id': 'asst_4jusMzpPNGElB2CHfRWQm9A9',\n",
       "   'created_at': 1699332881,\n",
       "   'description': None,\n",
       "   'file_ids': [],\n",
       "   'instructions': 'You are a personal math tutor. Write and run code to answer math questions.',\n",
       "   'metadata': {},\n",
       "   'model': 'gpt-4-1106-preview',\n",
       "   'name': 'Math Tutor',\n",
       "   'object': 'assistant',\n",
       "   'tools': [{'type': 'code_interpreter'}]},\n",
       "  {'id': 'asst_h55vmlImJD9zRgWI5hWieuyz',\n",
       "   'created_at': 1699320457,\n",
       "   'description': None,\n",
       "   'file_ids': [],\n",
       "   'instructions': 'You are a personal math tutor. Write and run code to answer math questions.',\n",
       "   'metadata': {},\n",
       "   'model': 'gpt-4-1106-preview',\n",
       "   'name': 'Math Tutor',\n",
       "   'object': 'assistant',\n",
       "   'tools': [{'type': 'code_interpreter'}]}],\n",
       " 'object': 'list',\n",
       " 'first_id': 'asst_4jusMzpPNGElB2CHfRWQm9A9',\n",
       " 'last_id': 'asst_h55vmlImJD9zRgWI5hWieuyz',\n",
       " 'has_more': False}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.beta.assistants.list().dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7e1b96fa-afd1-42c9-bbc9-9314ad2b27ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "assistant = client.beta.assistants.create(\n",
    "    name=\"Math Tutor\",\n",
    "    instructions=\"You are a personal math tutor. Write and run code to answer math questions.\",\n",
    "    tools=[{\"type\": \"code_interpreter\"}],\n",
    "    model=\"gpt-4-1106-preview\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e75c676d-2fd5-4e2e-a190-1bf892138aa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12575/3914797818.py:1: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.4/migration/\n",
      "  assistant.dict()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'id': 'asst_BKEvxRa8DpbMyROnnQ0oIpvL',\n",
       " 'created_at': 1699332913,\n",
       " 'description': None,\n",
       " 'file_ids': [],\n",
       " 'instructions': 'You are a personal math tutor. Write and run code to answer math questions.',\n",
       " 'metadata': {},\n",
       " 'model': 'gpt-4-1106-preview',\n",
       " 'name': 'Math Tutor',\n",
       " 'object': 'assistant',\n",
       " 'tools': [{'type': 'code_interpreter'}]}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assistant.dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "49029b61-7b40-4cab-b75a-fd20ad88ff85",
   "metadata": {},
   "outputs": [],
   "source": [
    "thread = client.beta.threads.create()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "817ace44-e0cb-413d-a5dc-6615a434c2d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "message = client.beta.threads.messages.create(\n",
    "    thread_id=thread.id,\n",
    "    role=\"user\",\n",
    "    content=\"I need to solve the equation `3x + 11 = 14`. Can you help me?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "45652766-5472-4ab8-b6f0-edaa9f0477a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12575/629163619.py:1: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.4/migration/\n",
      "  message.dict()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'id': 'msg_2TvfRsgYw0AtBrTt7xbCmBM0',\n",
       " 'assistant_id': None,\n",
       " 'content': [{'text': {'annotations': [],\n",
       "    'value': 'I need to solve the equation `3x + 11 = 14`. Can you help me?'},\n",
       "   'type': 'text'}],\n",
       " 'created_at': 1699332914,\n",
       " 'file_ids': [],\n",
       " 'metadata': {},\n",
       " 'object': 'thread.message',\n",
       " 'role': 'user',\n",
       " 'run_id': None,\n",
       " 'thread_id': 'thread_6r8Tw4bZB2nfoHLYtHIfKv5i'}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "message.dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b2a6cd28-9ae1-4943-a55a-e18c02285470",
   "metadata": {},
   "outputs": [],
   "source": [
    "run = client.beta.threads.runs.create(\n",
    "  thread_id=thread.id,\n",
    "  assistant_id=assistant.id,\n",
    "  instructions=\"Please address the user as Jane Doe. The user has a premium account.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c6305e6a-2743-41e9-a06c-a08a45a81349",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12575/322777259.py:1: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.4/migration/\n",
      "  run.dict()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'id': 'run_aRTF5pHhb4wYnZSbdYHkXQGl',\n",
       " 'assistant_id': 'asst_BKEvxRa8DpbMyROnnQ0oIpvL',\n",
       " 'cancelled_at': None,\n",
       " 'completed_at': None,\n",
       " 'created_at': 1699332914,\n",
       " 'expires_at': 1699333514,\n",
       " 'failed_at': None,\n",
       " 'file_ids': [],\n",
       " 'instructions': 'Please address the user as Jane Doe. The user has a premium account.',\n",
       " 'last_error': None,\n",
       " 'metadata': {},\n",
       " 'model': 'gpt-4-1106-preview',\n",
       " 'object': 'thread.run',\n",
       " 'required_action': None,\n",
       " 'started_at': None,\n",
       " 'status': 'queued',\n",
       " 'thread_id': 'thread_6r8Tw4bZB2nfoHLYtHIfKv5i',\n",
       " 'tools': [{'type': 'code_interpreter'}]}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a46934c1-c31a-48b5-ad17-22fc62ff2062",
   "metadata": {},
   "outputs": [],
   "source": [
    "run = client.beta.threads.runs.retrieve(\n",
    "  thread_id=thread.id,\n",
    "  run_id=run.id\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2cb5987c-c864-47b1-9eb8-7ba3d81e7eb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12575/322777259.py:1: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.4/migration/\n",
      "  run.dict()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'id': 'run_aRTF5pHhb4wYnZSbdYHkXQGl',\n",
       " 'assistant_id': 'asst_BKEvxRa8DpbMyROnnQ0oIpvL',\n",
       " 'cancelled_at': None,\n",
       " 'completed_at': None,\n",
       " 'created_at': 1699332914,\n",
       " 'expires_at': 1699333514,\n",
       " 'failed_at': None,\n",
       " 'file_ids': [],\n",
       " 'instructions': 'Please address the user as Jane Doe. The user has a premium account.',\n",
       " 'last_error': None,\n",
       " 'metadata': {},\n",
       " 'model': 'gpt-4-1106-preview',\n",
       " 'object': 'thread.run',\n",
       " 'required_action': None,\n",
       " 'started_at': 1699332914,\n",
       " 'status': 'in_progress',\n",
       " 'thread_id': 'thread_6r8Tw4bZB2nfoHLYtHIfKv5i',\n",
       " 'tools': [{'type': 'code_interpreter'}]}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6264191c-66ae-49bc-b16e-17c0cef12712",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = client.beta.threads.messages.list(\n",
    "  thread_id=thread.id\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4878939c-1417-49d8-896a-257bd2378527",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12575/3072803778.py:1: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.4/migration/\n",
      "  messages.dict()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'data': [{'id': 'msg_2TvfRsgYw0AtBrTt7xbCmBM0',\n",
       "   'assistant_id': None,\n",
       "   'content': [{'text': {'annotations': [],\n",
       "      'value': 'I need to solve the equation `3x + 11 = 14`. Can you help me?'},\n",
       "     'type': 'text'}],\n",
       "   'created_at': 1699332914,\n",
       "   'file_ids': [],\n",
       "   'metadata': {},\n",
       "   'object': 'thread.message',\n",
       "   'role': 'user',\n",
       "   'run_id': None,\n",
       "   'thread_id': 'thread_6r8Tw4bZB2nfoHLYtHIfKv5i'}],\n",
       " 'object': 'list',\n",
       " 'first_id': 'msg_2TvfRsgYw0AtBrTt7xbCmBM0',\n",
       " 'last_id': 'msg_2TvfRsgYw0AtBrTt7xbCmBM0',\n",
       " 'has_more': False}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e88faf79-f087-40a8-bd0b-3e11bda6a4b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3f4fd03d-c160-4467-a27b-adcedf88f66c",
   "metadata": {},
   "source": [
    "#### Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a2a3be47-0532-4292-9fa7-4493ac615c41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://openai.com/pricing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3078bf03-4339-48ca-b251-ba81e49c3262",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "842e1740-6e51-44d9-90d0-116d37b0edb4",
   "metadata": {},
   "source": [
    "#### Rate Limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fafa497-8cf7-449c-85bb-8de25ce60038",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://platform.openai.com/docs/guides/rate-limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53ef68c3-7339-452d-80cf-117c75e4785e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37534e76-5706-4880-97bd-1fe88bca32a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9ed5f3e3-74f8-48bf-87f1-c1ea6d40f3d6",
   "metadata": {},
   "source": [
    "## 总结和回顾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb486238-b0bb-43db-aa75-08e4463946b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1cd80e1-be8b-43e5-8cb0-a154cf1059f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e61822ae-cbe4-47de-a6a3-97b5bfdc022e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de2db34f-d05d-4b67-8a1b-d2076059d73a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e08e618-f027-4c83-b2ae-51917138f579",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f6aff29-1968-4ffe-b065-8e646c73b1a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1208a1a-4e3c-4d34-8529-91089740df4d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "mimetype": "text/x-python",
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